Debugging Claude Code Agents: Loops and Bad Tool Calls
Fix the three failure modes that break Claude coding agents: loops, wrong tool calls, and hallucinated arguments — with concrete harness-level tactics.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Fix the three failure modes that break Claude coding agents: loops, wrong tool calls, and hallucinated arguments — with concrete harness-level tactics.
What to put in a Claude coding agent's context and what to leave out: working-set curation, fixed vs fluid budgeting, and retrieval over pre-loading.
Connect MCP servers to Claude agents safely: tight schemas, server-side auth, structured retryable errors, and idempotency keys for safe retries.
Reusable patterns for Claude coding agents: layered prompts, orthogonal tools, plan-then-act, structured results, and summarize-and-evict context control.
Step-by-step build of a Claude coding agent: tool schemas, the turn loop, sandboxed execution, and a test-verification gate, with real code to adapt.
How Claude's coding agents work end to end — model, harness, context engine, tools, and the verification loop behind benchmark-leading results.
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How to configure, build, and use MCP (Model Context Protocol) servers with Claude Code — connecting databases, APIs, GitHub, Slack, and custom tools to your AI workflow.